My research program emphasizes in the exploration, development and implementation of control and estimation methods to address real-world problems via provably-correct solutions. I am particularly interested in multi-agent systems operating in uncertain environments (e.g., unstructured and/or adversarial).

Some of the ongoing and past research programs are described below.

Human-Robot Collaborative Networks

A Human-centric Network of Free-Flying Co-Robots: We envision to ultimately augment astronauts in spacewalk with the Astronet: A Network of Astronautical Free-Flying Co-Robots, such as SPHERES or Astrobees developed by NASA. The Astronet will safely surround the crew member during EVAs, infer human intent and interpret them into predefined tasks, and respond to human inputs by redistributing autonomously in space to dynamically and continually improve task conditions in a human-centric way. We link our dynamic coverage control paradigm for the swarm motion with inference methods to online learn the task-relevant cues of the human while executing multiple tasks. We also motion models and sensing uncertainty for the 3D flight of free floating robots, and energy consumption models capturing their limited power resources.

This research is sponsored by the NASA Space Technology Research Grants Program through an Early Career Faculty Award "https://www.nasa.gov/directorates/spacetech/strg/ecf2016/AstroNet.html"

On-the-Fly Assistive-View Learning in Augmented Reality Multitasking Environments

Human-Robot (Astrobee) Interaction in a Virtual Reality Environment of the International Space Station

Human-Robot Interaction for Dynamic Coverage via Gesture Following

Bayesian-inferred Human Intention and Flexible Robot Trajectory Generation

Interception and Herding of Risk-Averse Agents

We are studying and developing defense strategies against aerial swarms of small UAS. In our earlier work we studied secure (resilient) communication topologies that achieve resilient formation control for aerial swarms in the presence of malicious information from attackers. We then developed control laws for a swarm of defender agents that herd an attacking swarm to a safe area in obstacle-populated environments. More recently, we consider more complicated behaviors by the attackers, such as splitting into smaller swarms. Apart from herding solutions, we also have studied strategies for the defenders to capture/intercept the attackers. Our goal end is to combine herding and capturing solutions to provide a complete (under certain conditions) defending strategy against attackers.

This work has been funded by the Center for Unmanned Aircraft Systems (C-UAS), a National Science Foundation Industry/University Cooperative Research Center (I/UCRC) under NSF Award No. 1738714 along with significant contributions from CUAS industry members.

Multi-Swarm Herding

3D Herding

Swarm Herding (Gazebo)

Earlier Work on Multi-Robot Systems

Decentralized Goal Assignment and Trajectory Generation via Multiple Lyapunov Functions | Collaborative work with UPenn: We develop decentralized feedback control policies and coordination protocols for multi-robot systems with certain safety and performance guarantees. Safety is realized as the collision-free motion towards goal locations under restricted sensing and communication capabilities, while performance is realized as the assignment of goals which result in shortest total distance to the goal locations. The formulation within a Multiple Lyapunov-like Barrier Functions approach enables scalable and correct-by-construction algorithms, which perform well for hundreds of agents.

6 Agents

100 Agents

Visibility Maintenance for Leader-Follower Formations in Obstacle Environments: We consider GPS-denied obstacle environments where multiple robots need to coordinate their motion using vision-based sensing systems only, in the absence of explicit information exchange. Physical obstacles may obstruct visibility, therefore effective sensing, and furthermore should always be avoided.

We develop decentralized motion coordination algorithms for formations of mobile robots in such constrained environments, which guarantee the collision-free motion of the robotic network and the maintenance of visibility among robotic agents.

Dynamic Positioning and Formation Control for Underactuated Marine Vehicles: Guidance, navigation and control of marine vehicles (ships, surface vessels and underwater vehicles) is an active research topic, motivated in part by the extensive use of autonomous vehicles in oil industry, scientific explorations (e.g. in oceanographic, archaeological and marine biology research), search and rescue missions, surveillance and inspection tasks, etc. The underwater environment, in particular, poses additional challenges to guidance, navigation and control tasks due to the lack of GPS measurements. Thus, vision is often the main means of sensing and localization with respect to targets of interest.

We develop hybrid and switching control algorithms for underactuated vehicles which move in the presence of unknown external disturbances and vision-based constraints, which guarantee the practical stability of the system with respect to a target of interest. Trade-offs between visibility maintenance and accurate positioning are studied and analyzed. We also consider multi-vehicle formation control so that multiple marine vehicles maintain visibility with, and eventually encircle, a target of interest.

Our next goals include the extension of the methodologies to docking and collaborative control problems for satellites and spacecraft.